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An image processing approach to approximating interface textures of microcrystalline silicon layers grown on existing aluminum-doped zinc oxide textures

Kai Hertel, Jürgen Hüpkes, and Christoph Pflaum

Open Access Open Access

Abstract

We present an algorithm for generating a surface approximation of microcrystalline silicon (μc-Si) layers after plasma enhanced chemical vapor deposition (PECVD) onto surface textured substrates, where data of the textured substrate surface are available as input. We utilize mathematical image processing tools and combine them with an ellipsoid generator approach. The presented algorithm has been tuned for use in thin-film silicon solar cell applications, where textured surfaces are used to improve light trapping. We demonstrate the feasibility of this method by means of optical simulations of generated surface textures, comparing them to simulations of measured atomic force microscopy (AFM) scan data of both Aluminum-doped zinc oxide (AZO, a transparent and conductive material) and μc-Si layers.

© 2013 Optical Society of America

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Figures (11)
Fig. 1
Fig. 1 AFM texture data of AZO surface (a) with corresponding AFM μc-Si texture (b) courtesy of Forschungszentrum Jülich [27], and, for comparison, a texture generated from the AZO surface using our algorithm (c). Intensity corresponds to height in this depiction, with a range of 0.8μm.

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Fig. 2
Fig. 2 One peak benchmark with corresponding ellipsoid (a) and, for comparison, two peaks of elevation 1μm and 1 2 μ m respectively, with a resulting overlap of ellipsoids (b).

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Fig. 3
Fig. 3 One peak benchmark: Input data (a), and outputs of the sphere generator (top: plot over line with depiction of normalized curvature below) as well as resulting superimposed spheres (bottom half) after 1 (b), 2 (c), 3 (d) and 4 (e) iterations.

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Fig. 4
Fig. 4 Section of the input layer AZO AFM data (a), and 1 (b), 2 (c) and 3 (d) iterations of the sphere generator applied to it.

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Fig. 5
Fig. 5 Superposition of 2 (a) and 3 (b) sphere generator iterates from Figs. 4(b)–4(c) and 4(b)–4(d), respectively.

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Fig. 6
Fig. 6 Enhanced section of the AFM scan of the μc-Si texture from Fig. 1(b) for reference. This section corresponds to the AZO section used as input in Figs. 4, 5, and 7.

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Fig. 7
Fig. 7 Generated spheres (a) and roughness applied to it by means of a distribution of additional ellipsoids, ranging from 5nm (b) to 20nm (d) in vertical peak size. The corresponding root mean square roughness added by this step ranges from 21nm to 31nm.

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Fig. 8
Fig. 8 Histogram of the relative frequency of angle occurrences throughout the textures: A comparison of the AZO (red), μc-Si (green) and algorithmically generated textures (yellow). Angles are measured in degrees against the horizontal plane and quantized to integer values.

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Fig. 9
Fig. 9 Input AZO (A) with textures generated by the algorithm corresponding to simulation results (1) and (2) in Table 2, and μc-Si AFM scan for comparison (S).

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Fig. 10
Fig. 10 Simulation results: External quantum efficiencies across the optical spectrum. Results are based on FDFD optical simulations.

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Fig. 11
Fig. 11 Simulation results: Back contact absorption losses across the optical spectrum. Results are based on FDFD optical simulations.

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Tables (2)

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Table 1 Layer specifications of the simulated solar cells

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Table 2 Simulation results: Output current in the lower and higher parts of the optical regime, as well as combined values for the full spectrum. The resulting output of the shorter wavelengths is caused by incoupling, while light of longer wavelengths benefits from light trapping by means of the scattering properties of the bottom interface texture. Mean value and standard deviations correspond to simulations (1) through (5) with algorithm generated textures. Results are based on EQE results as depicted in Fig. 10 and the air mass 1.5 solar spectral irradiance (AM1.5).

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Equations (2)

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f ( u ) = − ∇ ⋅ ∇ u ‖ ∇ u ‖
∂ u ∂ t = ∇ ⋅ ∇ u
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